SoroushVahidi/scidocs huggingface dataset:
Eating when bored: revision of the emotional eating scale with a focus on boredom.
(2012)

OBJECTIVE
The current study explored whether eating when bored is a distinct construct from other negative emotions by revising the emotional eating scale (EES) to include a separate boredom factor. Additionally, the relative endorsement of eating when bored compared to eating in response to other negative emotions was examined.


METHOD
A convenience sample of 139 undergraduates completed open-ended questions regarding their behaviors when experiencing different levels of emotions. Participants were then given the 25-item EES with 6 additional items designed to measure boredom.


RESULTS
On the open-ended items, participants more often reported eating in response to boredom than the other emotions. Exploratory factor analysis showed that boredom is a separate construct from other negative emotions. Additionally, the most frequently endorsed item on the EES was "eating when bored".


CONCLUSIONS
These results suggest that boredom is an important construct, and that it should be considered a separate dimension of emotional eating.
Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM
(2014)

The detection of seizure activity in electroencephalogram (EEG) signals is crucial for the classification of epileptic seizures. However, epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recordings is highly required. In this work, we present a new technique for seizure classification of EEG signals using Hilbert–Huang transform (HHT) and support vector machine (SVM). In our method, the HHT based time-frequency representation (TFR) has been considered as time-frequency image (TFI), the segmentation of TFI has been implemented based on the frequency-bands of the rhythms of EEG signals, the histogram of grayscale sub-images has been represented. Statistical features such as ime-frequency image upport vector machine eizure classification mean, variance, skewness and kurtosis of pixel intensity in the histogram have been extracted. The SVM with radial basis function (RBF) kernel has been employed for classification of seizure and nonseizure EEG signals. The classification accuracy and receiver operating characteristics (ROC) curve have been used for evaluating the performance of the classifier. Experimental results show that the best average classification accuracy of this algorithm can reach 99.125% with the theta rhythm of EEG signals.
MagicFuzzer: Scalable deadlock detection for large-scale applications
(2012)

We present MagicFuzzer, a novel dynamic deadlock detection technique. Unlike existing techniques to locate potential deadlock cycles from an execution, it iteratively prunes lock dependencies that each has no incoming or outgoing edge. Combining with a novel thread-specific strategy, it dramatically shrinks the size of lock dependency set for cycle detection, improving the efficiency and scalability of such a detection significantly. In the real deadlock confirmation phase, it uses a new strategy to actively schedule threads of an execution against the whole set of potential deadlock cycles. We have implemented a prototype and evaluated it on large-scale C/C++ programs. The experimental results confirm that our technique is significantly more effective and efficient than existing techniques.
Positive Definite Kernels in Machine Learning
(2009)

This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functions {k(x, ·), x ∈ X} associated with a kernel k defined on a space X . We discuss at length the construction of kernel functions that take advantage of well-known statistical models. We provide an overview of numerous data-analysis methods which take advantage of reproducing kernel Hilbert spaces and discuss the idea of combining several kernels to improve the performance on certain tasks. We also provide a short cookbook of different kernels which are particularly useful for certain datatypes such as images, graphs or speech segments. Remark: This report is a draft. I apologize in advance for the numerous mistakes, typos, and not always well written parts it contains. Comments and suggestions will be highly appreciated.
Single imputation with multilayer perceptron and multiple imputation combining multilayer perceptron and k-nearest neighbours for monotone patterns
(2015)

The knowledge discovery process is supported by data files information gathered from collected data sets, which often contain errors in the form of missing values. Data imputation is the activity aimed at estimating values for missing data items. This study focuses on the development of automated data imputation models, based on artificial neural networks for monotone patterns of missing values. The present work proposes a single imputation approach relying on a multilayer perceptron whose training is conducted with different learning rules, and a multiple imputation approach based on the combination of multilayer perceptron and k-nearest neighbours. Eighteen real and simulated databases were exposed to a perturbation experiment with random generation of monotone missing data pattern. An empirical test ean/mode model ultilayer perceptron egression model was accomplished on these data sets, including both approaches (single and multiple imputations), and three classical single imputation procedures – mean/mode imputation, regression and hot-deck – were also considered. Therefore, the experiments involved five imputation methods. The results, considering different performance measures, demonstrated that, in comparison with traditional tools, both proposals improve the automation level and data quality offering a satisfactory performance.
Who Uses Bitcoin? An exploration of the Bitcoin community
(2014)

Many cryptocurrencies have come into existence in recent years, with Bitcoin the most prominent among them. Although its short history has been volatile, the virtual currency maintains a core group of committed users. This paper presents an exploratory analysis of Bitcoin users. As a virtual currency and peer-to-peer payment system, Bitcoin may signal future challenges to state oversight and financial powers through its decentralized structure and offer of instantaneous transactions with relative anonymity. Very little is known about the users of Bitcoin, however. Utilizing publicly available survey data of Bitcoin users, this analysis explores the structure of the Bitcoin community in terms of wealth accumulation, optimism about the future of Bitcoin, and themes that attract users to the cryptocurrency. Results indicate that age, time of initial use, geographic location, mining status, engaging online discourse, and political orientation are all relevant factors that help explain various aspects of Bitcoin wealth, optimism, and attraction.
3-D Reciprocal Collision Avoidance on Physical Quadrotor Helicopters with On-Board Sensing for Relative Positioning
(2014)

In this paper, we present an implementation of 3D reciprocal collision avoidance on real quadrotor helicopters where each quadrotor senses the relative position and veloc ity of other quadrotors using an on-board camera. We show that using our approach, quadrotors are able to successfully avo id pairwise collisions GPS and motion-capture denied environ ments, without communication between the quadrotors, and even when human operators deliberately attempt to induce collision. To our knowledge, this is the first time that reciprocal collision avoidance has been successfully implemented on r eal robots where each agent independently observes the others using on-board sensors. We theoretically analyze the respo nse of the collision-avoidance algorithm to the violated assumptions by the use of real robots. We quantitatively analyze our experimental results. A particularly striking observation is that at times the quadrotors exhibit “reciprocal dance” behavior, which is also observed when humans move past each other in constrained environments. This seems to be the result of sensing uncertainty, which causes both robots involved to h ave a different belief about the relative positions and velocit ies and, as a result, choose the same side on which to pass.
Zerocash: Decentralized Anonymous Payments from Bitcoin
(2014)

Bit coin is the first digital currency to see widespread adoption. While payments are conducted between pseudonyms, Bit coin cannot offer strong privacy guarantees: payment transactions are recorded in a public decentralized ledger, from which much information can be deduced. Zero coin (Miers et al., IEEE S&P 2013) tackles some of these privacy issues by unlinking transactions from the payment's origin. Yet, it still reveals payments' destinations and amounts, and is limited in functionality. In this paper, we construct a full-fledged ledger-based digital currency with strong privacy guarantees. Our results leverage recent advances in zero-knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs). First, we formulate and construct decentralized anonymous payment schemes (DAP schemes). A DAP scheme enables users to directly pay each other privately: the corresponding transaction hides the payment's origin, destination, and transferred amount. We provide formal definitions and proofs of the construction's security. Second, we build Zero cash, a practical instantiation of our DAP scheme construction. In Zero cash, transactions are less than 1 kB and take under 6 ms to verify - orders of magnitude more efficient than the less-anonymous Zero coin and competitive with plain Bit coin.
Metadata Embeddings for User and Item Cold-start Recommendations
(2015)

I present a hybrid matrix factorisation model representing users and items as linear combinations of their content features’ latent factors. The model outperforms both collaborative and content-based models in cold-start or sparse interaction data scenarios (using both user and item metadata), and performs at least as well as a pure collaborative matrix factorisation model where interaction data is abundant. Additionally, feature embeddings produced by the model encode semantic information in a way reminiscent of word embedding approaches, making them useful for a range of related tasks such as tag recommendations.
Extremely Low-Profile, Single-Arm, Wideband Spiral Antenna Radiating a Circularly Polarized Wave
(2010)

The antenna characteristics are described of a single-arm spiral (SAS) antenna. A balun circuit, required for a conventional two-arm balanced-mode spiral (TAS), is not necessary for feeding this SAS. First, the radiation pattern of an SAS having a disc/ground-plane is investigated. When the radius of the disc is smaller than the radius of the first-mode active region on the spiral and the spacing between the disc and spiral plane is small, the SAS is found to radiate a circularly polarized (CP) bidirectional beam, whose radiation pattern is almost symmetric with respect to the antenna axis normal to the spiral plane. Second, from a practical standpoint, the CP bidirectional beam is transformed into a CP unidirectional beam by placing a conducting cavity behind an SAS with a disc. It is revealed that the SAS has a good VSWR (less than 2) and a good axial ratio (less than 3 dB) over a design frequency range of 3 GHz to 10 GHz, where a cavity/antenna height of 7 mm (0.07 wavelength at the lowest design frequency) is chosen. The frequency response of the gain for the SAS is found to be similar to that for a conventional TAS, and the radiation efficiency for the SAS is slightly larger than that for the TAS. It is concluded that the SAS with a small disc backed by a cavity realizes a circularly polarized, low-profile, wideband antenna with a simple feed system that does not require a balun circuit.
Vocabulary acquisition from extensive reading : A case study
(2006)

A number of studies have shown that second language learners acquire vocabulary through reading, but only relatively small amounts. However, most of these studies used only short texts, measured only the acquisition of meaning, and did not credit partial learning of words. This case study of a learner of French explores whether an extensive reading program can enhance lexical knowledge. The study assessed a relatively large number of words (133), and examined whether one month of extensive reading enhanced knowledge of these target words' spelling, meaning, and grammatical characteristics. The measurement procedure was a one-on-one interview that allowed a very good indication of whether learning occurred. The study also explores how vocabulary acquisition varies according to how often words are encountered in the texts. The results showed that knowledge of 65% of the target words was enhanced in some way, for a pickup rate of about 1 of every 1.5 words tested. Spelling was strongly enhanced, even from a small number of exposures. Meaning and grammatical knowledge were also enhanced, but not to the same extent. Overall, the study indicates that more vocabulary acquisition is possible from extensive reading than previous studies have suggested.
Gated End-to-End Memory Networks
(2017)

Machine reading using differentiable reasoning models has recently shown remarkable progress. In this context, End-to-End trainable Memory Networks (MemN2N) have demonstrated promising performance on simple natural language based reasoning tasks such as factual reasoning and basic deduction. However, other tasks, namely multi-fact questionanswering, positional reasoning or dialog related tasks, remain challenging particularly due to the necessity of more complex interactions between the memory and controller modules composing this family of models. In this paper, we introduce a novel end-to-end memory access regulation mechanism inspired by the current progress on the connection short-cutting principle in the field of computer vision. Concretely, we develop a Gated End-toEnd trainable Memory Network architecture (GMemN2N). From the machine learning perspective, this new capability is learned in an end-to-end fashion without the use of any additional supervision signal which is, as far as our knowledge goes, the first of its kind. Our experiments show significant improvements on the most challenging tasks in the 20 bAbI dataset, without the use of any domain knowledge. Then, we show improvements on the Dialog bAbI tasks including the real human-bot conversion-based Dialog State Tracking Challenge (DSTC-2) dataset. On these two datasets, our model sets the new state of the art. ∗work done as an Intern at XRCE
Feature Selection for Maximizing the Area Under the ROC Curve
(2009)

Feature selection is an important pre-processing step for solving classification problems. A good feature selection method may not only improve the performance of the final classifier, but also reduce the computational complexity of it. Traditionally, feature selection methods were developed to maximize the classification accuracy of a classifier. Recently, both theoretical and experimental studies revealed that a classifier with the highest accuracy might not be ideal in real-world problems. Instead, the Area Under the ROC Curve (AUC) has been suggested as the alternative metric, and many existing learning algorithms have been modified in order to seek the classifier with maximum AUC. However, little work was done to develop new feature selection methods to suit the requirement of AUC maximization. To fill this gap in the literature, we propose in this paper a novel algorithm, called AUC and Rank Correlation coefficient Optimization (ARCO) algorithm. ARCO adopts the general framework of a well-known method, namely minimal redundancy- maximal-relevance (mRMR) criterion, but defines the terms ”relevance” and ”redundancy” in totally different ways. Such a modification looks trivial from the perspective of algorithmic design. Nevertheless, experimental study on four gene expression data sets showed that feature subsets obtained by ARCO resulted in classifiers with significantly larger AUC than the feature subsets obtained by mRMR. Moreover, ARCO also outperformed the Feature Assessment by Sliding Thresholds algorithm, which was recently proposed for AUC maximization, and thus the efficacy of ARCO was validated.
Overconfidence and Trading Volume
(2003)

Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors. Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data seems to be a promising way to better understand which psychological phenomena actually drive economic behavior.
Defining Computational Thinking for Mathematics and Science Classrooms
(2016)

Science and mathematics are becoming computational endeavors. This fact is reflected in the recently released Next Generation Science Standards and the decision to include ‘‘computational thinking’’ as a core scientific practice. With this addition, and the increased presence of computation in mathematics and scientific contexts, a new urgency has come to the challenge of defining computational thinking and providing a theoretical grounding for what form it should take in school science and mathematics classrooms. This paper presents a response to this challenge by proposing a definition of computational thinking for mathematics and science in the form of a taxonomy consisting of four main categories: data practices, modeling and simulation practices, computational problem solving practices, and systems thinking practices. In formulating this taxonomy, we draw on the existing computational thinking literature, interviews with mathematicians and scientists, and exemplary computational thinking instructional materials. This work was undertaken as part of a larger effort to infuse computational thinking into high school science and mathematics curricular materials. In this paper, we argue for the approach of embedding computational thinking in mathematics and science contexts, present the taxonomy, and discuss how we envision the taxonomy being used to bring current educational efforts in line with the increasingly computational nature of modern science and mathematics.
Flexible Learning on the Sphere Via Adaptive Needlet Shrinkage and Selection
(2009)

This paper introduces an approach for flexible, robust Bayesian modeling of structure in spherical data sets. Our method is based upon a recent construction called the needlet, which is a particular form of spherical wavelet with many favorable statistical and computational properties. We perform shrinkage and selection of needlet coefficients, focusing on two main alternatives: empirical-Bayes thresholding for selection, and the horseshoe prior for shrinkage. We study the performance of the proposed methodology both on simulated data and on a real data set involving the cosmic microwave background radiation. Horseshoe shrinkage of needlet coefficients is shown to yield the best overall performance against some common benchmarks.
A privacy-compliant fingerprint recognition system based on homomorphic encryption and Fingercode templates
(2010)

The privacy protection of the biometric data is an important research topic, especially in the case of distributed biometric systems. In this scenario, it is very important to guarantee that biometric data cannot be steeled by anyone, and that the biometric clients are unable to gather any information different from the single user verification/identification. In a biométrie system with high level of privacy compliance, also the server that processes the biométrie matching should not learn anything on the database and it should be impossible for the server to exploit the resulting matching values in order to extract any knowledge about the user presence or behavior. Within this conceptual framework, in this paper we propose a novel complete demonstrator based on a distributed biométrie system that is capable to protect the privacy of the individuals by exploiting cryptosystems. The implemented system computes the matching task in the encrypted domain by exploiting homomorphic encryption and using Fingercode templates. The paper describes the design methodology of the demonstrator and the obtained results. The demonstrator has been fully implemented and tested in real applicative conditions. Experimental results show that this method is feasible in the cases where the privacy of the data is more important than the accuracy of the system and the obtained computational time is satisfactory.
Extended Kalman filter based grid synchronization in the presence of voltage unbalance for smart grid
(2011)

In this paper, grid synchronization for gird-connected power generation systems in the presence of voltage unbalance and frequency variation is considered. A new extended Kalman filter based synchronization algorithm is proposed to track the phase angle of the utility network. Instead of processing the three-phase voltage signal in the abc natural reference frame and resorting to the symmetrical component transformation as in the traditional way, the proposed algorithm separates the positive and negative sequences in the transformed αβ stationary reference frame. Based on the obtained expressions in αβ domain, an EKF is developed to track both the in-phase and quadrature sinusoidal signals together with the unknown frequency. An estimate of the phase angle of the positive sequence is then obtained. As a by-product, estimates of phase angle of negative sequence and grid frequency are also computed. Compared to the commonly used scheme, the proposed algorithm has a simplified structure. The good performance is supported by computer simulations.